iNeuron Internship Project : Swiggy Data Analysis

BY : Prateek Magdum

# library import
import pandas as pd
import numpy as np
import random as rnd

# for data visualization 
import seaborn as sns 
import matplotlib.pyplot as plt
%matplotlib inline


import plotly.express as px

import warnings
warnings.filterwarnings(action = 'ignore')

df=pd.read_csv("D:/Prateek/Intersnship_Data/Dataset/Swiggy Bangalore Outlet Details.csv")
df

	Shop_Name	Cuisine	Location	Rating	Cost_for_Two
0	Kanti Sweets	Sweets	Koramangala, Koramangala	4.3	₹ 150
1	Mumbai Tiffin	North Indian, Home Food, Thalis, Combo	Sector 5, HSR	4.4	₹ 400
2	Sri Krishna sagar	South Indian, North Indian, Fast Food, Beverag...	6th Block, Koramangala	4.1	₹ 126
3	Al Daaz	American, Arabian, Chinese, Desserts, Fast Foo...	HSR, HSR	4.4	₹ 400
4	Beijing Bites	Chinese, Thai	5th Block, Koramangala	4.1	₹ 450
...	...	...	...	...	...
113	Wok Paper Scissors	Pan-Asian, Chinese, Asian	JNC Road, Koramangala	3.9	₹ 219
114	Savoury Restaurant	Arabian, Middle Eastern, North Indian, Grill, ...	Madiwala, BTM	4.1	₹ 600
115	Royal Treat	North Indian, Chinese, Seafood, Biryani	5th block Koramangala, Koramangala	4.2	₹ 193
116	Thali 99	North Indian	Koramangala, Koramangala	4.3	₹ 200
117	Mani's Dum Biryani	Andhra, Biryani	1st Block, Koramangala	4.2	₹ 400

118 rows × 5 columns

df.describe()

	Shop_Name	Cuisine	Location	Rating	Cost_for_Two
count	118	118	118	118	118
unique	115	79	65	13	30
top	Leon Grill	North Indian	BTM, BTM	4.1	₹ 300
freq	2	12	13	30	16

df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 118 entries, 0 to 117
Data columns (total 5 columns):
 #   Column        Non-Null Count  Dtype 
---  ------        --------------  ----- 
 0   Shop_Name     118 non-null    object
 1   Cuisine       118 non-null    object
 2   Location      118 non-null    object
 3   Rating        118 non-null    object
 4   Cost_for_Two  118 non-null    object
dtypes: object(5)
memory usage: 4.7+ KB

df.isnull().sum()

Shop_Name       0
Cuisine         0
Location        0
Rating          0
Cost_for_Two    0
dtype: int64

Data Pre-processing & Cleaning -

df['Shop_Name'].unique()

array(['Kanti Sweets', 'Mumbai Tiffin', 'Sri Krishna sagar', 'Al Daaz',
       'Beijing Bites', 'Kitchens of Punjab',
       '99 VARIETY DOSA AND PAV BHAJI- Malli Mane Food Court',
       "La Pino'z Pizza", 'Hotel Manu', 'Yumlane Pizza',
       'Ambur Star Briyani', 'Cake Box', 'Meghana Foods', 'Momoz',
       'A2B - Adyar Ananda Bhavan', 'Shawarma Inc',
       'WarmOven Cake & Desserts', 'Sri Lakshmi Dhaba',
       'Falahaar & Kota Kachori', 'Shree Khana Khazana',
       'Just Bake - Cakes & confectioners', 'Maa Di Hatti',
       'Hotel Godavari', 'Rolls On Wheels', 'New Udupi Grand',
       'Swad Punjab da', 'Rice Bowl', 'High N Hungry', 'Burger King',
       'Nandhana Palace', 'Easy Bites', 'Bengali Fun Foods',
       'Madurai Idly Shop', 'Oottupura', 'Taco Bell',
       'Hyderabadi Biryani Hub', 'Biriyani Zone', "Gongura's",
       'Bathinda Junction', 'Leon Grill', "Venu's Donne Biryani",
       'Cakewala', 'Swadista Aahar', 'Cream Stone',
       'Svadu Pure Ghee Sweets', 'Sai Abhiruchi', 'Pizza Hut',
       'Hyderabad Biryaani House', 'China Pearl', "Balaji's Veg",
       'Onesta', 'Donne Biryani Mandi', 'FreshMenu',
       'Donne Biryani House', 'Pipabu', 'Sharmas punjabi restaurant',
       'KFC', 'Donne Biriyani House', "Nanda's", 'Sagar fast food',
       'calicut cafe restaurant', 'Cake Garden', 'Biryanis and more',
       'Biryani Khazana', '800 Momos', 'World of asia', 'Ghar Ka Khana',
       'Thalassery Restaurant', 'KANNUR FOOD POINT', 'KANNOOR RESTAURANT',
       'Fattoush', 'Nizams Biryani', 'Nagarjuna', 'Punjabi Rasoi',
       'CRAVY WINGS - The American Diner', 'Barista',
       'BIRIYANI TASTE MASTH(BTM)', 'Khichdi Experiment',
       'Tandoori Merchant', 'Chinese Bae', 'Kitchens of China',
       'Natural Ice Cream', 'Abhiruchi Hotel', 'Punjabi Swag', 'Truffles',
       'Gyaani Da Punjabi Dhaba', 'Biriyani Bhatti', 'Khawa Karpo',
       '99 VARIETY DOSA AND JUICE-Malli mane food court',
       'Purani Dilli By Anand Sweets', 'XO Belgian Waffle',
       'BIRYANI CRAFTS', 'Madeena Hotel', 'Corner House Ice Cream',
       'NIC Natural Ice Creams', 'Just Shawarma',
       'Mandya Gowdru Donne Biryani', 'Dindigul Thalapakatti Biriyani',
       'Delhi Food Point', 'Mad Over Donuts', 'Village Donne Biriyani',
       'R.B Food Point', 'Paradise Biryani', 'New Tasty Cafeteria',
       'Junior Kuppanna', 'The Bowl Company',
       'Hotel Savitha Family Restaurant', 'Biryani Pot', 'Bowl 99',
       'Kritunga', 'Wok Paper Scissors', 'Savoury Restaurant',
       'Royal Treat', 'Thali 99', "Mani's Dum Biryani"], dtype=object)

df.Shop_Name.value_counts()

Leon Grill                    2
La Pino'z Pizza               2
Easy Bites                    2
Falahaar & Kota Kachori       1
Ambur Star Briyani            1
                             ..
Shawarma Inc                  1
Onesta                        1
Sharmas punjabi restaurant    1
Venu's Donne Biryani          1
Royal Treat                   1
Name: Shop_Name, Length: 115, dtype: int64

df.Rating.unique()

array(['4.3', '4.4', '4.1', '4.2', '3.9', '3.8', '4', '3.7', '3.6', '4.8',
       '4.5', '4.6', '--'], dtype=object)

df.dtypes

Shop_Name       object
Cuisine         object
Location        object
Rating          object
Cost_for_Two    object
dtype: object

df.Rating=df.Rating.str.replace('--','0').astype(float)

df.Rating.unique()

array([4.3, 4.4, 4.1, 4.2, 3.9, 3.8, 4. , 3.7, 3.6, 4.8, 4.5, 4.6, 0. ])

df.Cost_for_Two.unique()

array(['₹ 150', '₹ 400', '₹ 126', '₹ 450', '₹ 350', '₹ 200', '₹ 500',
       '₹ 247', '₹ 550', '₹ 300', '₹ 129', '₹ 250', '₹ 268', '₹ 600',
       '₹ 527', '₹ 130', '₹ 257', '₹ 280', '₹ 399', '₹ 220', '₹ 800',
       '₹ 100', '₹ 178', '₹ 120', '₹ 251', '₹ 650', '₹ 132', '₹ 153',
       '₹ 219', '₹ 193'], dtype=object)

a=df.Cost_for_Two
a.unique()

array(['₹ 150', '₹ 400', '₹ 126', '₹ 450', '₹ 350', '₹ 200', '₹ 500',
       '₹ 247', '₹ 550', '₹ 300', '₹ 129', '₹ 250', '₹ 268', '₹ 600',
       '₹ 527', '₹ 130', '₹ 257', '₹ 280', '₹ 399', '₹ 220', '₹ 800',
       '₹ 100', '₹ 178', '₹ 120', '₹ 251', '₹ 650', '₹ 132', '₹ 153',
       '₹ 219', '₹ 193'], dtype=object)

obj=lambda x:x[2:]
    

a=a.apply(obj)

a

0      150
1      400
2      126
3      400
4      450
      ... 
113    219
114    600
115    193
116    200
117    400
Name: Cost_for_Two, Length: 118, dtype: object

a=a.astype(int)
a

0      150
1      400
2      126
3      400
4      450
      ... 
113    219
114    600
115    193
116    200
117    400
Name: Cost_for_Two, Length: 118, dtype: int32

df.Cost_for_Two=a
df.Cost_for_Two

0      150
1      400
2      126
3      400
4      450
      ... 
113    219
114    600
115    193
116    200
117    400
Name: Cost_for_Two, Length: 118, dtype: int32

df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 118 entries, 0 to 117
Data columns (total 5 columns):
 #   Column        Non-Null Count  Dtype  
---  ------        --------------  -----  
 0   Shop_Name     118 non-null    object 
 1   Cuisine       118 non-null    object 
 2   Location      118 non-null    object 
 3   Rating        118 non-null    float64
 4   Cost_for_Two  118 non-null    int32  
dtypes: float64(1), int32(1), object(3)
memory usage: 4.3+ KB

df.describe()

	Rating	Cost_for_Two
count	118.000000	118.000000
mean	4.061864	321.008475
std	0.430845	137.286804
min	0.000000	100.000000
25%	4.000000	204.750000
50%	4.100000	300.000000
75%	4.300000	400.000000
max	4.800000	800.000000

Observation

1. Mean Rating for Swiggy is 4.061 which is good for Customers.

2. Average Cost for two person is approximately 321 rupees.

3. Max Rating and Cost for two is 4.8 and 800 rupees respectively.

#Shops that have minimum cost and maximum rating

df.sort_values(by='Rating', ascending=False).head()

	Shop_Name	Cuisine	Location	Rating	Cost_for_Two
78	Khichdi Experiment	Home Food, Healthy Food, Indian	Koramangala, Koramangala	4.8	200
94	Corner House Ice Cream	Ice Cream, Desserts	7th Block, Koramangala	4.6	250
82	Natural Ice Cream	Ice Cream	Near Wipro Park Signal, Koramangala	4.6	150
80	Chinese Bae	Chinese, Thai	BTM, BTM	4.5	450
3	Al Daaz	American, Arabian, Chinese, Desserts, Fast Foo...	HSR, HSR	4.4	400

df.sort_values(by='Cost_for_Two').head()

	Shop_Name	Cuisine	Location	Rating	Cost_for_Two
79	Tandoori Merchant	Andhra, Biryani, Chinese, Desserts, Fast Food,...	4th Cross, BTM	4.2	100
89	99 VARIETY DOSA AND JUICE-Malli mane food court	Fast Food, Juices, North Indian	Koramangala 1st block, Koramangala	4.1	100
95	NIC Natural Ice Creams	Ice Cream, Desserts	Koramangla, Koramangala	4.2	120
2	Sri Krishna sagar	South Indian, North Indian, Fast Food, Beverag...	6th Block, Koramangala	4.1	126
21	Maa Di Hatti	Chinese, Healthy Food, North Indian	Jakkasandra Extn, Koramangala	4.0	129

Observation

1. Most liked food is from Khichdi Experiment which have Home Food, Healthy Food, Indian cuisines and have highest rating of 4.8

2. Ice cream business is more liked in Koramangala

3. Minimum cost for two is From shop "Maa di Hatti"

plt.figure(figsize=(15,15))
plt.xticks(rotation=90)
sns.barplot(x=df.Shop_Name,y=df.Cost_for_Two)

<AxesSubplot:xlabel='Shop_Name', ylabel='Cost_for_Two'>

plt.figure(figsize=(15,15))
plt.xticks(rotation=90)
sns.barplot(x=df.Shop_Name,y=df.Rating)

<AxesSubplot:xlabel='Shop_Name', ylabel='Rating'>

df.Rating.describe()

count    118.000000
mean       4.061864
std        0.430845
min        0.000000
25%        4.000000
50%        4.100000
75%        4.300000
max        4.800000
Name: Rating, dtype: float64

df["Rating"].sort_values(ascending=True)

98     0.0
103    3.6
71     3.6
17     3.7
84     3.7
      ... 
1      4.4
80     4.5
94     4.6
82     4.6
78     4.8
Name: Rating, Length: 118, dtype: float64

Area-wise Analysis

L=df.Location.unique()
L

array(['Koramangala, Koramangala', 'Sector 5, HSR',
       '6th Block, Koramangala', 'HSR, HSR', '5th Block, Koramangala',
       'Koramangala 4th  Block, Koramangala', 'BTM 2nd Stage, BTM',
       'BTM, BTM', '9th Main road, Koramangala', 'outer ring road, BTM',
       '7th Block, Koramangala', '1st MAin, Koramangala',
       'Bommanahalli, BTM', '6th block, Koramangala', 'Sector 4, HSR',
       'BTM 1st stage, BTM', 'Jakkasandra Extn, Koramangala',
       'Marutinagar Main Road, BTM', '1st Block, Koramangala',
       '4th Cross, BTM', 'koramangala, Koramangala', 'BTM 2nd stage, BTM',
       '3rd main, BTM', 'HSR 1st sector, HSR', 'Sector 7, HSR',
       '3rd Sector, HSR', 'Chocolate Factory Road, BTM',
       '16th Main Road, 2nd Stage, BTM', '1st Stage, BTM',
       'Hosur Main Road, Koramangala',
       '1st Cross Road, 5th Block, Near Jyothi Nivas College, Koramangala',
       'Mico Layout, BTM', '4th Cross, Koramangala',
       '4th Block, Koramangala', 'Intermediate Ring Road, Koramangala',
       '3rd sector, HSR', '8TH BLOCK, Koramangala',
       '4th b cross, Koramangala', 'SG palaya, BTM',
       "Venkatapura Main Rd, Teacher's Colony, Jakkasandra, HSR",
       'KHB Colony, Koramangala', 'Sector 3, HSR',
       'Bannerghatta Road, Jayanagar',
       '80 Feet Peripheral Road, Koramangala', 'Btm, BTM',
       'Near Wipro Park Signal, Koramangala', '16th Main Road, BTM',
       '2nd Stage, BTM', 'Kuvempu Nagar, Stage 2, BTM',
       'Koramangala 1st block, Koramangala',
       '5th Block Kormangala, Koramangala', 'Koramangla, Koramangala',
       '5th block, Koramangala', '9th Main Rd, Sector 6, HSR Layout, HSR',
       'Jay Bheema Nagar, BTM', 'Koramangala 6th block, Koramangala',
       'Maruthi Nagar, BTM', 'Sector 6, HSR',
       'Jakkasandra Village, Koramangala', '4th block, Koramangala',
       'Madiwala Junction, BTM', 'kormangala, Koramangala',
       'JNC Road, Koramangala', 'Madiwala, BTM',
       '5th block Koramangala, Koramangala'], dtype=object)

loc=[]
def dis():
    for i in df.Location:
        loc.append(i.split(",")[-1])
    return loc
l=dis()
l

[' Koramangala',
 ' HSR',
 ' Koramangala',
 ' HSR',
 ' Koramangala',
 ' Koramangala',
 ' BTM',
 ' BTM',
 ' HSR',
 ' Koramangala',
 ' BTM',
 ' Koramangala',
 ' Koramangala',
 ' Koramangala',
 ' Koramangala',
 ' Koramangala',
 ' Koramangala',
 ' BTM',
 ' Koramangala',
 ' HSR',
 ' BTM',
 ' Koramangala',
 ' BTM',
 ' Koramangala',
 ' HSR',
 ' BTM',
 ' Koramangala',
 ' BTM',
 ' Koramangala',
 ' Koramangala',
 ' Koramangala',
 ' BTM',
 ' Koramangala',
 ' BTM',
 ' Koramangala',
 ' BTM',
 ' HSR',
 ' HSR',
 ' Koramangala',
 ' HSR',
 ' BTM',
 ' HSR',
 ' BTM',
 ' Koramangala',
 ' BTM',
 ' BTM',
 ' Koramangala',
 ' Koramangala',
 ' Koramangala',
 ' BTM',
 ' Koramangala',
 ' BTM',
 ' Koramangala',
 ' Koramangala',
 ' Koramangala',
 ' Koramangala',
 ' Koramangala',
 ' HSR',
 ' HSR',
 ' Koramangala',
 ' BTM',
 ' HSR',
 ' Koramangala',
 ' Koramangala',
 ' Koramangala',
 ' BTM',
 ' BTM',
 ' Koramangala',
 ' BTM',
 ' BTM',
 ' BTM',
 ' HSR',
 ' Koramangala',
 ' HSR',
 ' Jayanagar',
 ' Koramangala',
 ' BTM',
 ' Koramangala',
 ' Koramangala',
 ' BTM',
 ' BTM',
 ' Koramangala',
 ' Koramangala',
 ' BTM',
 ' BTM',
 ' Koramangala',
 ' BTM',
 ' BTM',
 ' Koramangala',
 ' Koramangala',
 ' Koramangala',
 ' Koramangala',
 ' BTM',
 ' Koramangala',
 ' Koramangala',
 ' Koramangala',
 ' Koramangala',
 ' Koramangala',
 ' HSR',
 ' HSR',
 ' Koramangala',
 ' HSR',
 ' Koramangala',
 ' Koramangala',
 ' BTM',
 ' Koramangala',
 ' BTM',
 ' HSR',
 ' Koramangala',
 ' Koramangala',
 ' BTM',
 ' Koramangala',
 ' Koramangala',
 ' Koramangala',
 ' BTM',
 ' Koramangala',
 ' Koramangala',
 ' Koramangala']

a=set(l)
len(a)

4

a

{' BTM', ' HSR', ' Jayanagar', ' Koramangala'}

df["Area"]=l
df

	Shop_Name	Cuisine	Location	Rating	Cost_for_Two	Area
0	Kanti Sweets	Sweets	Koramangala, Koramangala	4.3	150	Koramangala
1	Mumbai Tiffin	North Indian, Home Food, Thalis, Combo	Sector 5, HSR	4.4	400	HSR
2	Sri Krishna sagar	South Indian, North Indian, Fast Food, Beverag...	6th Block, Koramangala	4.1	126	Koramangala
3	Al Daaz	American, Arabian, Chinese, Desserts, Fast Foo...	HSR, HSR	4.4	400	HSR
4	Beijing Bites	Chinese, Thai	5th Block, Koramangala	4.1	450	Koramangala
...	...	...	...	...	...	...
113	Wok Paper Scissors	Pan-Asian, Chinese, Asian	JNC Road, Koramangala	3.9	219	Koramangala
114	Savoury Restaurant	Arabian, Middle Eastern, North Indian, Grill, ...	Madiwala, BTM	4.1	600	BTM
115	Royal Treat	North Indian, Chinese, Seafood, Biryani	5th block Koramangala, Koramangala	4.2	193	Koramangala
116	Thali 99	North Indian	Koramangala, Koramangala	4.3	200	Koramangala
117	Mani's Dum Biryani	Andhra, Biryani	1st Block, Koramangala	4.2	400	Koramangala

118 rows × 6 columns

Area Koramangala

Koramangala = df[df['Area'] == ' Koramangala']
Koramangala

	Shop_Name	Cuisine	Location	Rating	Cost_for_Two	Area
0	Kanti Sweets	Sweets	Koramangala, Koramangala	4.3	150	Koramangala
2	Sri Krishna sagar	South Indian, North Indian, Fast Food, Beverag...	6th Block, Koramangala	4.1	126	Koramangala
4	Beijing Bites	Chinese, Thai	5th Block, Koramangala	4.1	450	Koramangala
5	Kitchens of Punjab	North Indian	Koramangala 4th Block, Koramangala	4.2	350	Koramangala
9	Yumlane Pizza	Pizzas, Italian, Mexican	9th Main road, Koramangala	3.8	150	Koramangala
...	...	...	...	...	...	...
112	Kritunga	Andhra, Biryani	5th Block, Koramangala	3.9	500	Koramangala
113	Wok Paper Scissors	Pan-Asian, Chinese, Asian	JNC Road, Koramangala	3.9	219	Koramangala
115	Royal Treat	North Indian, Chinese, Seafood, Biryani	5th block Koramangala, Koramangala	4.2	193	Koramangala
116	Thali 99	North Indian	Koramangala, Koramangala	4.3	200	Koramangala
117	Mani's Dum Biryani	Andhra, Biryani	1st Block, Koramangala	4.2	400	Koramangala

64 rows × 6 columns

Koramangala.describe()

	Rating	Cost_for_Two
count	64.000000	64.000000
mean	4.126563	297.828125
std	0.217666	134.319632
min	3.600000	100.000000
25%	4.000000	200.000000
50%	4.100000	254.000000
75%	4.300000	399.250000
max	4.800000	600.000000

plt.figure(figsize=(10,10))
plt.xticks(rotation=90)
sns.barplot(x=Koramangala['Shop_Name'],y=Koramangala.Rating)

<AxesSubplot:xlabel='Shop_Name', ylabel='Rating'>

sns.histplot(Koramangala['Cost_for_Two'],bins=15)

<AxesSubplot:xlabel='Cost_for_Two', ylabel='Count'>

sns.histplot(Koramangala['Rating'],bins=10)

<AxesSubplot:xlabel='Rating', ylabel='Count'>

Area Jayanagar

Jayanagar = df[df['Area'] == ' Jayanagar']
Jayanagar

	Shop_Name	Cuisine	Location	Rating	Cost_for_Two	Area
74	CRAVY WINGS - The American Diner	American, Fast Food	Bannerghatta Road, Jayanagar	4.3	300	Jayanagar

Area HSR

HSR = df[df['Area'] == ' HSR']
HSR

	Shop_Name	Cuisine	Location	Rating	Cost_for_Two	Area
1	Mumbai Tiffin	North Indian, Home Food, Thalis, Combo	Sector 5, HSR	4.4	400	HSR
3	Al Daaz	American, Arabian, Chinese, Desserts, Fast Foo...	HSR, HSR	4.4	400	HSR
8	Hotel Manu	South Indian, Kerala, Chinese, North Indian	HSR, HSR	4.1	350	HSR
19	Shree Khana Khazana	Indian, Rajasthani	Sector 4, HSR	4.1	350	HSR
24	New Udupi Grand	Chinese, Jain, North Indian, South Indian	HSR, HSR	4.3	150	HSR
36	Biriyani Zone	North Indian, Chinese, Biryani	HSR 1st sector, HSR	4.1	600	HSR
37	Gongura's	North Indian, Chinese, Biryani	Sector 7, HSR	3.8	300	HSR
39	Leon Grill	Turkish, Portuguese, American	3rd Sector, HSR	4.3	300	HSR
41	Cakewala	Desserts	HSR, HSR	4.3	450	HSR
57	Donne Biriyani House	South Indian	3rd sector, HSR	4.0	300	HSR
58	Nanda's	Andhra, Biryani	HSR, HSR	4.0	400	HSR
61	Cake Garden	Desserts, Bakery	HSR, HSR	3.9	250	HSR
71	Nizams Biryani	Biryani, Juices, Kebabs	Venkatapura Main Rd, Teacher's Colony, Jakkasa...	3.6	200	HSR
73	Punjabi Rasoi	North Indian	Sector 3, HSR	4.0	800	HSR
98	Mandya Gowdru Donne Biryani	Biryani	HSR, HSR	0.0	350	HSR
99	Dindigul Thalapakatti Biriyani	North Indian	HSR, HSR	4.1	650	HSR
101	Easy Bites	Snacks, American	9th Main Rd, Sector 6, HSR Layout, HSR	3.8	200	HSR
107	Junior Kuppanna	Chettinad, South Indian	Sector 6, HSR	4.0	550	HSR

HSR.describe()

	Rating	Cost_for_Two
count	18.000000	18.000000
mean	3.844444	388.888889
std	0.983524	169.389523
min	0.000000	150.000000
25%	3.925000	300.000000
50%	4.050000	350.000000
75%	4.250000	437.500000
max	4.400000	800.000000

plt.figure(figsize=(10,10))
plt.xticks(rotation=90)
sns.barplot(x=HSR['Shop_Name'],y=HSR.Rating)

<AxesSubplot:xlabel='Shop_Name', ylabel='Rating'>

sns.histplot(HSR['Cost_for_Two'],bins=10)

<AxesSubplot:xlabel='Cost_for_Two', ylabel='Count'>

sns.histplot(HSR['Rating'],bins=10)

<AxesSubplot:xlabel='Rating', ylabel='Count'>

Area BTM

BTM = df[df['Area'] == ' BTM']
BTM

	Shop_Name	Cuisine	Location	Rating	Cost_for_Two	Area
6	99 VARIETY DOSA AND PAV BHAJI- Malli Mane Food...	Fast Food, North Indian, Chinese	BTM 2nd Stage, BTM	4.1	200	BTM
7	La Pino'z Pizza	Italian	BTM, BTM	3.9	500	BTM
10	Ambur Star Briyani	Chinese, South Indian, North Indian, Desserts,...	outer ring road, BTM	4.1	500	BTM
17	Sri Lakshmi Dhaba	North Indian	Bommanahalli, BTM	3.7	200	BTM
20	Just Bake - Cakes & confectioners	Desserts, Bakery	BTM 1st stage, BTM	4.3	300	BTM
22	Hotel Godavari	North Indian, Chinese, Hyderabadi	Marutinagar Main Road, BTM	4.0	400	BTM
25	Swad Punjab da	Indian	BTM, BTM	4.1	250	BTM
27	High N Hungry	Andhra, Biryani, Chinese, Desserts, Fast Food,...	4th Cross, BTM	4.1	350	BTM
31	Bengali Fun Foods	North Indian	BTM 2nd stage, BTM	4.2	300	BTM
33	Oottupura	Kerala, South Indian	BTM, BTM	4.3	268	BTM
35	Hyderabadi Biryani Hub	North Indian, Chinese, Biryani	3rd main, BTM	3.9	450	BTM
40	Venu's Donne Biryani	Biryani	Chocolate Factory Road, BTM	4.3	300	BTM
42	Swadista Aahar	South Indian, Snacks, North Indian, Chinese	16th Main Road, 2nd Stage, BTM	4.1	250	BTM
44	Svadu Pure Ghee Sweets	Desserts, Fast Food, Sweets, Chaat	1st Stage, BTM	4.1	200	BTM
45	Sai Abhiruchi	Chinese, South Indian, Andhra, Hyderabadi	BTM, BTM	3.7	250	BTM
49	Balaji's Veg	North Indian, Chinese, South Indian	Mico Layout, BTM	4.1	300	BTM
51	Donne Biryani Mandi	Biryani, Andhra, South Indian	BTM, BTM	4.0	150	BTM
60	calicut cafe restaurant	Fast Food, Beverages	BTM, BTM	4.1	280	BTM
65	World of asia	Beverages, Chinese	BTM, BTM	4.0	250	BTM
66	Ghar Ka Khana	North Indian	BTM, BTM	4.2	220	BTM
68	KANNUR FOOD POINT	Kerala, Chinese	SG palaya, BTM	3.9	300	BTM
69	KANNOOR RESTAURANT	North Indian, Chinese	BTM, BTM	4.0	250	BTM
70	Fattoush	Arabian, Beverages, Biryani, Chinese, Desserts...	BTM, BTM	3.9	400	BTM
76	BIRIYANI TASTE MASTH(BTM)	North Indian, South Indian	Btm, BTM	4.2	300	BTM
79	Tandoori Merchant	Andhra, Biryani, Chinese, Desserts, Fast Food,...	4th Cross, BTM	4.2	100	BTM
80	Chinese Bae	Chinese, Thai	BTM, BTM	4.5	450	BTM
83	Abhiruchi Hotel	Chinese, Hyderabadi, Biryani, Indian, South In...	BTM, BTM	4.0	250	BTM
84	Punjabi Swag	Punjabi, North Indian, Chinese, Fast Food, Hea...	16th Main Road, BTM	3.7	400	BTM
86	Gyaani Da Punjabi Dhaba	North Indian	2nd Stage, BTM	4.0	500	BTM
87	Biriyani Bhatti	Biryani, Hyderabadi, Andhra, North Indian, Sou...	Kuvempu Nagar, Stage 2, BTM	4.1	350	BTM
92	BIRYANI CRAFTS	Indian	BTM, BTM	4.1	500	BTM
104	R.B Food Point	Chinese, North Indian	Jay Bheema Nagar, BTM	3.7	350	BTM
106	New Tasty Cafeteria	Andhra, Chettinad, Chinese, Mughlai, North Indian	Maruthi Nagar, BTM	4.0	350	BTM
110	Biryani Pot	North Indian, Biryani	Madiwala Junction, BTM	4.0	500	BTM
114	Savoury Restaurant	Arabian, Middle Eastern, North Indian, Grill, ...	Madiwala, BTM	4.1	600	BTM

HSR.describe()

	Rating	Cost_for_Two
count	18.000000	18.000000
mean	3.844444	388.888889
std	0.983524	169.389523
min	0.000000	150.000000
25%	3.925000	300.000000
50%	4.050000	350.000000
75%	4.250000	437.500000
max	4.400000	800.000000

plt.figure(figsize=(10,10))
plt.xticks(rotation=90)
sns.barplot(x=BTM['Shop_Name'],y=BTM.Rating)

<AxesSubplot:xlabel='Shop_Name', ylabel='Rating'>

sns.histplot(BTM['Cost_for_Two'],bins=10)

<AxesSubplot:xlabel='Cost_for_Two', ylabel='Count'>

sns.histplot(BTM['Rating'],bins=10)

<AxesSubplot:xlabel='Rating', ylabel='Count'>

Conclusion:

As we can see that the Area-wise Rating & Cost for Two varies as following:

BTM : Most has 4.0 to 4.2 Rating and Approx. Cost for Two People lies between 200 to 350. (Max. Cost goes upto 600)

HSR : Most has 4 or above Rating and Approx. Cost for Two People lies between 300 to 400. (Max. Cost goes upto 800)

Koramangala : Most has 4.0 to 4.3 Rating and Approx. Cost for Two People lies between 200 to 350. (Max. Cost goes upto 600)

With this we can conclude the Most Costly Area is HSR

Revenue Area wise

Revenue={}

Revenue['BTM']=BTM['Cost_for_Two'].sum()
Revenue['HSR']=HSR['Cost_for_Two'].sum()
Revenue['Jayanagar']=Jayanagar['Cost_for_Two'].sum()
Revenue['Koramangala']=Koramangala['Cost_for_Two'].sum()

Re=Revenue.values()
city=Revenue.keys()
    
Revenue=pd.DataFrame()
Revenue['Revenue'] = Re
Revenue['City'] = city
Revenue

	Revenue	City
0	11518	BTM
1	7000	HSR
2	300	Jayanagar
3	19061	Koramangala

sns.barplot(x=Revenue['City'], y=Revenue['Revenue'],data=Revenue)
plt.xlabel('Revenue ------>',fontsize=15)
plt.ylabel('City    ------>',fontsize=15)
plt.show()

sns.countplot(df.Area)
plt.title("Number of shop In Area")

Text(0.5, 1.0, 'Number of shop In Area')

Conclusion:

Maximum Revenue is from Koramangala and Maximum number of shops are from Koramangala

CUISINE ANALYSIS:

df.Cuisine

0                                                 Sweets
1                 North Indian, Home Food, Thalis, Combo
2      South Indian, North Indian, Fast Food, Beverag...
3      American, Arabian, Chinese, Desserts, Fast Foo...
4                                          Chinese, Thai
                             ...                        
113                            Pan-Asian, Chinese, Asian
114    Arabian, Middle Eastern, North Indian, Grill, ...
115              North Indian, Chinese, Seafood, Biryani
116                                         North Indian
117                                      Andhra, Biryani
Name: Cuisine, Length: 118, dtype: object

w=df.Cuisine.unique()
w

array(['Sweets', 'North Indian, Home Food, Thalis, Combo',
       'South Indian, North Indian, Fast Food, Beverages, Jain',
       'American, Arabian, Chinese, Desserts, Fast Food, Mughlai, North Indian',
       'Chinese, Thai', 'North Indian',
       'Fast Food, North Indian, Chinese', 'Italian',
       'South Indian, Kerala, Chinese, North Indian',
       'Pizzas, Italian, Mexican',
       'Chinese, South Indian, North Indian, Desserts, Fast Food, Kerala, Andhra, Beverages, Mughlai, Seafood',
       'Desserts', 'Chinese, Andhra, Biryani, Seafood', 'Chinese',
       'South Indian, Chinese, Desserts, North Indian',
       'Arabian, Fast Food', 'Desserts, Beverages', 'Indian, Rajasthani',
       'Desserts, Bakery', 'Chinese, Healthy Food, North Indian',
       'North Indian, Chinese, Hyderabadi', 'Fast Food',
       'Chinese, Jain, North Indian, South Indian', 'Indian',
       'North Indian, South Indian, Chinese',
       'Andhra, Biryani, Chinese, Desserts, Fast Food, Seafood, South Indian',
       'American, Fast Food',
       'Biryani, Seafood, North Indian, Chinese, Desserts, Andhra, South Indian',
       'Snacks, American', 'South Indian', 'Kerala, South Indian',
       'Mexican', 'North Indian, Chinese, Biryani',
       'Turkish, Portuguese, American', 'Biryani',
       'South Indian, Snacks, North Indian, Chinese',
       'Desserts, Fast Food, Sweets, Chaat',
       'Chinese, South Indian, Andhra, Hyderabadi', 'Pizzas, Fast Food',
       'Biryani, Mughlai, South Indian', 'Chinese, Asian',
       'North Indian, Chinese, South Indian', 'Italian, Desserts, Pizzas',
       'Biryani, Andhra, South Indian',
       'Chinese, Continental, Italian, Mediterranean, Thai, Lebanese, American, Asian, Beverages, Bakery, Biryani, Cafe, Desserts, Healthy Food, Mexican, North Indian, Salads, Pizzas',
       'Pizzas, Chinese, Pastas, Salads, American, Continental',
       'Andhra, Biryani',
       'Chinese, South Indian, North Indian, Fast Food',
       'Fast Food, Beverages',
       'Biryani, South Indian, North Indian, Fast Food, Andhra, Beverages, Mughlai, Seafood, Punjabi, Hyderabadi, Chinese',
       'Beverages, Chinese',
       'South Indian, Biryani, Kerala, North Indian, Chinese',
       'Kerala, Chinese', 'North Indian, Chinese',
       'Arabian, Beverages, Biryani, Chinese, Desserts, North Indian',
       'Biryani, Juices, Kebabs', 'Andhra, South Indian',
       'Beverages, Cafe, Snacks', 'North Indian, South Indian',
       'Turkish, Portuguese, American, Grill',
       'Home Food, Healthy Food, Indian', 'Ice Cream',
       'Chinese, Hyderabadi, Biryani, Indian, South Indian, Andhra, Tandoor',
       'Punjabi, North Indian, Chinese, Fast Food, Healthy Food, Mughlai, Desserts',
       'American',
       'Biryani, Hyderabadi, Andhra, North Indian, South Indian',
       'Fast Food, Juices, North Indian',
       'North Indian, Chaat, Snacks, Fast Food',
       'Desserts, Mughlai, Seafood', 'Ice Cream, Desserts',
       'Chinese, North Indian', 'Biryani, Kebabs',
       'Andhra, Chettinad, Chinese, Mughlai, North Indian',
       'Chettinad, South Indian',
       'Continental, Indian, Pan-Asian, Oriental',
       'North Indian, Biryani', 'Pan-Asian, Chinese, Asian',
       'Arabian, Middle Eastern, North Indian, Grill, Seafood, Kerala, Chinese',
       'North Indian, Chinese, Seafood, Biryani'], dtype=object)

cu=[]
for i in w:
    e=i.split(",")
    for j in e:
        if j[0]==" ":
            j=j[1:]
        if j not in cu:
            cu.append(j)

cu

['Sweets',
 'North Indian',
 'Home Food',
 'Thalis',
 'Combo',
 'South Indian',
 'Fast Food',
 'Beverages',
 'Jain',
 'American',
 'Arabian',
 'Chinese',
 'Desserts',
 'Mughlai',
 'Thai',
 'Italian',
 'Kerala',
 'Pizzas',
 'Mexican',
 'Andhra',
 'Seafood',
 'Biryani',
 'Indian',
 'Rajasthani',
 'Bakery',
 'Healthy Food',
 'Hyderabadi',
 'Snacks',
 'Turkish',
 'Portuguese',
 'Chaat',
 'Asian',
 'Continental',
 'Mediterranean',
 'Lebanese',
 'Cafe',
 'Salads',
 'Pastas',
 'Punjabi',
 'Juices',
 'Kebabs',
 'Grill',
 'Ice Cream',
 'Tandoor',
 'Chettinad',
 'Pan-Asian',
 'Oriental',
 'Middle Eastern']

dict_Cuisine = {}
for i in  df['Cuisine'].unique():
    Cuisines_Lists = i.split(',')
    for Cuisine in Cuisines_Lists:
        Cuisine =Cuisine.lstrip(" ")
        if Cuisine in dict_Cuisine:
            dict_Cuisine[Cuisine] = dict_Cuisine[Cuisine] + 1
        else: 
            dict_Cuisine[Cuisine] = 1
        
print(dict_Cuisine)
print()
print('Total Records: \t', len( dict_Cuisine))

{'Sweets': 2, 'North Indian': 32, 'Home Food': 2, 'Thalis': 1, 'Combo': 1, 'South Indian': 23, 'Fast Food': 16, 'Beverages': 9, 'Jain': 2, 'American': 8, 'Arabian': 4, 'Chinese': 35, 'Desserts': 15, 'Mughlai': 7, 'Thai': 2, 'Italian': 4, 'Kerala': 6, 'Pizzas': 5, 'Mexican': 3, 'Andhra': 12, 'Seafood': 8, 'Biryani': 18, 'Indian': 5, 'Rajasthani': 1, 'Bakery': 2, 'Healthy Food': 4, 'Hyderabadi': 5, 'Snacks': 4, 'Turkish': 2, 'Portuguese': 2, 'Chaat': 2, 'Asian': 3, 'Continental': 3, 'Mediterranean': 1, 'Lebanese': 1, 'Cafe': 2, 'Salads': 2, 'Pastas': 1, 'Punjabi': 2, 'Juices': 2, 'Kebabs': 2, 'Grill': 2, 'Ice Cream': 2, 'Tandoor': 1, 'Chettinad': 2, 'Pan-Asian': 2, 'Oriental': 1, 'Middle Eastern': 1}

Total Records: 	 48

Cuisine = dict_Cuisine.keys()
freq = dict_Cuisine.values()

df_Cuisine_Analysis = pd.DataFrame()

df_Cuisine_Analysis['Cuisine'] = Cuisine
df_Cuisine_Analysis['Count'] = freq

df_Cuisine_Analysis

	Cuisine	Count
0	Sweets	2
1	North Indian	32
2	Home Food	2
3	Thalis	1
4	Combo	1
5	South Indian	23
6	Fast Food	16
7	Beverages	9
8	Jain	2
9	American	8
10	Arabian	4
11	Chinese	35
12	Desserts	15
13	Mughlai	7
14	Thai	2
15	Italian	4
16	Kerala	6
17	Pizzas	5
18	Mexican	3
19	Andhra	12
20	Seafood	8
21	Biryani	18
22	Indian	5
23	Rajasthani	1
24	Bakery	2
25	Healthy Food	4
26	Hyderabadi	5
27	Snacks	4
28	Turkish	2
29	Portuguese	2
30	Chaat	2
31	Asian	3
32	Continental	3
33	Mediterranean	1
34	Lebanese	1
35	Cafe	2
36	Salads	2
37	Pastas	1
38	Punjabi	2
39	Juices	2
40	Kebabs	2
41	Grill	2
42	Ice Cream	2
43	Tandoor	1
44	Chettinad	2
45	Pan-Asian	2
46	Oriental	1
47	Middle Eastern	1

plt.figure(figsize = (20, 8))
sns.barplot(x = df_Cuisine_Analysis['Cuisine'], 
            y = df_Cuisine_Analysis['Count'], 
            data = df_Cuisine_Analysis)
plt.xticks(rotation = 90)
plt.title('Cuisines Overall Analysis (Bangalore)', fontsize = 14, fontweight = 'bold', fontstyle = 'italic')
plt.xlabel('Cuisine', fontsize = 11, fontweight = 'bold')
plt.ylabel('Number of Restaurants', fontsize = 11, fontweight = 'bold')
plt.show()

df_Cuisine_sort=df_Cuisine_Analysis.sort_values(by='Count',ascending=False).head(10)
df_Cuisine_sort

	Cuisine	Count
11	Chinese	35
1	North Indian	32
5	South Indian	23
21	Biryani	18
6	Fast Food	16
12	Desserts	15
19	Andhra	12
7	Beverages	9
9	American	8
20	Seafood	8

plt.pie(df_Cuisine_sort['Count'],labels=df_Cuisine_sort['Cuisine'],autopct='%.2f%%')
plt.show()

Conclusion:

1. From the above Visualizations, We can say, Most of the Resturants sell "Chinese" which is around '19.89' followed by "North Indian" & "South Indian" Cuisines which are around '18.18' & '13.07'.
2. We can also infer that Most of the people are fond of these Cusines.

Cuisines :Area-wise Analysis:

BTM Area

BTM_cuisine=BTM["Cuisine"]
BTM_cuisine

6                       Fast Food, North Indian, Chinese
7                                                Italian
10     Chinese, South Indian, North Indian, Desserts,...
17                                          North Indian
20                                      Desserts, Bakery
22                     North Indian, Chinese, Hyderabadi
25                                                Indian
27     Andhra, Biryani, Chinese, Desserts, Fast Food,...
31                                          North Indian
33                                  Kerala, South Indian
35                        North Indian, Chinese, Biryani
40                                               Biryani
42           South Indian, Snacks, North Indian, Chinese
44                    Desserts, Fast Food, Sweets, Chaat
45             Chinese, South Indian, Andhra, Hyderabadi
49                   North Indian, Chinese, South Indian
51                         Biryani, Andhra, South Indian
60                                  Fast Food, Beverages
65                                    Beverages, Chinese
66                                          North Indian
68                                       Kerala, Chinese
69                                 North Indian, Chinese
70     Arabian, Beverages, Biryani, Chinese, Desserts...
76                            North Indian, South Indian
79     Andhra, Biryani, Chinese, Desserts, Fast Food,...
80                                         Chinese, Thai
83     Chinese, Hyderabadi, Biryani, Indian, South In...
84     Punjabi, North Indian, Chinese, Fast Food, Hea...
86                                          North Indian
87     Biryani, Hyderabadi, Andhra, North Indian, Sou...
92                                                Indian
104                                Chinese, North Indian
106    Andhra, Chettinad, Chinese, Mughlai, North Indian
110                                North Indian, Biryani
114    Arabian, Middle Eastern, North Indian, Grill, ...
Name: Cuisine, dtype: object

BTM_dict={}
for i in BTM_cuisine:
    k=i.split(",")
    for j in k:
        j = j.lstrip(' ')
        if j in BTM_dict:
            BTM_dict[j]=BTM_dict[j]+1
        else:
            BTM_dict[j]=1
            
print(BTM_dict)
print()
print('Total Records of BTM: \t', len(BTM_dict))   

{'Fast Food': 7, 'North Indian': 19, 'Chinese': 19, 'Italian': 1, 'South Indian': 11, 'Desserts': 7, 'Kerala': 4, 'Andhra': 8, 'Beverages': 4, 'Mughlai': 3, 'Seafood': 4, 'Bakery': 1, 'Hyderabadi': 4, 'Indian': 3, 'Biryani': 9, 'Snacks': 1, 'Sweets': 1, 'Chaat': 1, 'Arabian': 2, 'Thai': 1, 'Tandoor': 1, 'Punjabi': 1, 'Healthy Food': 1, 'Chettinad': 1, 'Middle Eastern': 1, 'Grill': 1}

Total Records of BTM: 	 26

Cuisine_BTM = BTM_dict.keys()
freq_BTM = BTM_dict.values()
dict_BTM = {
    'Cuisine' : Cuisine_BTM,
    'Count' : freq_BTM
}

BTM_Cuisine= pd.DataFrame(dict_BTM)
BTM_Cuisine

	Cuisine	Count
0	Fast Food	7
1	North Indian	19
2	Chinese	19
3	Italian	1
4	South Indian	11
5	Desserts	7
6	Kerala	4
7	Andhra	8
8	Beverages	4
9	Mughlai	3
10	Seafood	4
11	Bakery	1
12	Hyderabadi	4
13	Indian	3
14	Biryani	9
15	Snacks	1
16	Sweets	1
17	Chaat	1
18	Arabian	2
19	Thai	1
20	Tandoor	1
21	Punjabi	1
22	Healthy Food	1
23	Chettinad	1
24	Middle Eastern	1
25	Grill	1

plt.figure(figsize = (20, 8))
sns.barplot(x = BTM_Cuisine['Cuisine'], 
            y = BTM_Cuisine['Count'], 
            data = BTM_Cuisine)

plt.xticks(rotation = 90)

plt.title('Cuisines Analysis - BTM', fontsize = 14, fontweight = 'bold', fontstyle = 'italic')
plt.xlabel('Cuisine', fontsize = 11, fontweight = 'bold')
plt.ylabel('Number of Restaurants', fontsize = 11, fontweight = 'bold')

plt.show()

cu_BTM=BTM_Cuisine.sort_values(by="Count",ascending=False)

cu_BTM

	Cuisine	Count
2	Chinese	19
1	North Indian	19
4	South Indian	11
14	Biryani	9
7	Andhra	8
0	Fast Food	7
5	Desserts	7
12	Hyderabadi	4
6	Kerala	4
8	Beverages	4
10	Seafood	4
13	Indian	3
9	Mughlai	3
18	Arabian	2
11	Bakery	1
3	Italian	1
15	Snacks	1
16	Sweets	1
17	Chaat	1
19	Thai	1
20	Tandoor	1
21	Punjabi	1
22	Healthy Food	1
23	Chettinad	1
24	Middle Eastern	1
25	Grill	1

fig = px.pie(data_frame = cu_BTM, 
             names = cu_BTM['Cuisine'], 
             values = cu_BTM['Count'], 
             title = 'Distribution of Cuisines in Area BTM Restaurants')

fig.update_traces(textposition = 'inside', textinfo = 'percent+label')
fig.show()

Conclusion:

From the above Visualizations, We can say, In BTM Area, Most of the Resturants sell "Chinese" which is around '16.4%' followed by "North Indian" & "South Indian" Cuisines which are around '16.4%' & '9.48%'.

So, We can also infer that Most of the people are fond of these Cusines.

Area HSR

HSR_cuisine=HSR["Cuisine"]
HSR_cuisine

1                 North Indian, Home Food, Thalis, Combo
3      American, Arabian, Chinese, Desserts, Fast Foo...
8            South Indian, Kerala, Chinese, North Indian
19                                    Indian, Rajasthani
24             Chinese, Jain, North Indian, South Indian
36                        North Indian, Chinese, Biryani
37                        North Indian, Chinese, Biryani
39                         Turkish, Portuguese, American
41                                              Desserts
57                                          South Indian
58                                       Andhra, Biryani
61                                      Desserts, Bakery
71                               Biryani, Juices, Kebabs
73                                          North Indian
98                                               Biryani
99                                          North Indian
101                                     Snacks, American
107                              Chettinad, South Indian
Name: Cuisine, dtype: object

HSR_dict={}
for i in HSR_cuisine:
    k=i.split(",")
    for j in k:
        j = j.lstrip(' ')
        if j in HSR_dict:
            HSR_dict[j]=HSR_dict[j]+1
        else:
            HSR_dict[j]=1
            
print(HSR_dict)
print()
print('Total Records of HSR: \t', len(HSR_dict))   

{'North Indian': 8, 'Home Food': 1, 'Thalis': 1, 'Combo': 1, 'American': 3, 'Arabian': 1, 'Chinese': 5, 'Desserts': 3, 'Fast Food': 1, 'Mughlai': 1, 'South Indian': 4, 'Kerala': 1, 'Indian': 1, 'Rajasthani': 1, 'Jain': 1, 'Biryani': 5, 'Turkish': 1, 'Portuguese': 1, 'Andhra': 1, 'Bakery': 1, 'Juices': 1, 'Kebabs': 1, 'Snacks': 1, 'Chettinad': 1}

Total Records of HSR: 	 24

Cuisine_HSR = HSR_dict.keys()
freq_HSR= HSR_dict.values()
dict_HSR = {
    'Cuisine' : Cuisine_HSR,
    'Count' : freq_HSR
}

HSR_Cuisine= pd.DataFrame(dict_HSR)
HSR_Cuisine

	Cuisine	Count
0	North Indian	8
1	Home Food	1
2	Thalis	1
3	Combo	1
4	American	3
5	Arabian	1
6	Chinese	5
7	Desserts	3
8	Fast Food	1
9	Mughlai	1
10	South Indian	4
11	Kerala	1
12	Indian	1
13	Rajasthani	1
14	Jain	1
15	Biryani	5
16	Turkish	1
17	Portuguese	1
18	Andhra	1
19	Bakery	1
20	Juices	1
21	Kebabs	1
22	Snacks	1
23	Chettinad	1

plt.figure(figsize = (20, 8))
sns.barplot(x = HSR_Cuisine['Cuisine'], 
            y = HSR_Cuisine['Count'], 
            data = HSR_Cuisine)

plt.xticks(rotation = 90)

plt.title('Cuisines Analysis - HSR', fontsize = 14, fontweight = 'bold', fontstyle = 'italic')
plt.xlabel('Cuisine', fontsize = 11, fontweight = 'bold')
plt.ylabel('Number of Restaurants', fontsize = 11, fontweight = 'bold')

plt.show()

cu_HSR=HSR_Cuisine.sort_values(by="Count",ascending=False)

cu_HSR

	Cuisine	Count
0	North Indian	8
6	Chinese	5
15	Biryani	5
10	South Indian	4
4	American	3
7	Desserts	3
14	Jain	1
22	Snacks	1
21	Kebabs	1
20	Juices	1
19	Bakery	1
18	Andhra	1
17	Portuguese	1
16	Turkish	1
12	Indian	1
13	Rajasthani	1
1	Home Food	1
11	Kerala	1
9	Mughlai	1
8	Fast Food	1
5	Arabian	1
3	Combo	1
2	Thalis	1
23	Chettinad	1

fig = px.pie(data_frame = cu_HSR, 
             names = cu_HSR['Cuisine'], 
             values = cu_HSR['Count'], 
             title = 'Distribution of Cuisines in Area HSR Restaurants')

fig.update_traces(textposition = 'inside', textinfo = 'percent+label')
fig.show()

Conclusion:

We can say, In HSR Area, "North Indian" Cuisines are dominated by around '17.4%' followed by "Chinese" & "South Indian" Cuisines '10.9%' & '8.7%' Restaurants respectively.

In HSR Area, We may have more "North Indian" people staying there.

Koramangala Area

Koramangala_cuisine=Koramangala["Cuisine"]
Koramangala_cuisine

0                                                 Sweets
2      South Indian, North Indian, Fast Food, Beverag...
4                                          Chinese, Thai
5                                           North Indian
9                               Pizzas, Italian, Mexican
                             ...                        
112                                      Andhra, Biryani
113                            Pan-Asian, Chinese, Asian
115              North Indian, Chinese, Seafood, Biryani
116                                         North Indian
117                                      Andhra, Biryani
Name: Cuisine, Length: 64, dtype: object

Koramangala_dict={}
for i in Koramangala_cuisine:
    k=i.split(",")
    for j in k:
        j = j.lstrip(' ')
        if j in Koramangala_dict:
            Koramangala_dict[j]=Koramangala_dict[j]+1
        else:
            Koramangala_dict[j]=1
            
print(Koramangala_dict)
print()
print('Total Records of HSR: \t', len(Koramangala_dict))   

{'Sweets': 1, 'South Indian': 13, 'North Indian': 20, 'Fast Food': 11, 'Beverages': 5, 'Jain': 1, 'Chinese': 18, 'Thai': 2, 'Pizzas': 5, 'Italian': 4, 'Mexican': 3, 'Desserts': 12, 'Andhra': 6, 'Biryani': 12, 'Seafood': 5, 'Arabian': 1, 'Healthy Food': 3, 'American': 7, 'Snacks': 3, 'Mughlai': 3, 'Asian': 3, 'Continental': 3, 'Mediterranean': 1, 'Lebanese': 1, 'Bakery': 1, 'Cafe': 2, 'Salads': 2, 'Pastas': 1, 'Punjabi': 1, 'Hyderabadi': 1, 'Kerala': 1, 'Turkish': 1, 'Portuguese': 1, 'Grill': 1, 'Home Food': 1, 'Indian': 2, 'Ice Cream': 3, 'Juices': 1, 'Chaat': 1, 'Kebabs': 1, 'Pan-Asian': 2, 'Oriental': 1}

Total Records of HSR: 	 42

Cuisine_Koramangala = Koramangala_dict.keys()
freq_Koramangala= Koramangala_dict.values()
dict_Koramangala = {
    'Cuisine' : Cuisine_Koramangala,
    'Count' : freq_Koramangala
}

Koramangala_Cuisine= pd.DataFrame(dict_Koramangala)
Koramangala_Cuisine

	Cuisine	Count
0	Sweets	1
1	South Indian	13
2	North Indian	20
3	Fast Food	11
4	Beverages	5
5	Jain	1
6	Chinese	18
7	Thai	2
8	Pizzas	5
9	Italian	4
10	Mexican	3
11	Desserts	12
12	Andhra	6
13	Biryani	12
14	Seafood	5
15	Arabian	1
16	Healthy Food	3
17	American	7
18	Snacks	3
19	Mughlai	3
20	Asian	3
21	Continental	3
22	Mediterranean	1
23	Lebanese	1
24	Bakery	1
25	Cafe	2
26	Salads	2
27	Pastas	1
28	Punjabi	1
29	Hyderabadi	1
30	Kerala	1
31	Turkish	1
32	Portuguese	1
33	Grill	1
34	Home Food	1
35	Indian	2
36	Ice Cream	3
37	Juices	1
38	Chaat	1
39	Kebabs	1
40	Pan-Asian	2
41	Oriental	1

plt.figure(figsize = (20, 8))
sns.barplot(x = Koramangala_Cuisine['Cuisine'], 
            y = Koramangala_Cuisine['Count'], 
            data =Koramangala_Cuisine)

plt.xticks(rotation = 90)

plt.title('Cuisines Analysis - HSR', fontsize = 14, fontweight = 'bold', fontstyle = 'italic')
plt.xlabel('Cuisine', fontsize = 11, fontweight = 'bold')
plt.ylabel('Number of Restaurants', fontsize = 11, fontweight = 'bold')

plt.show()

cu_Koramangala=Koramangala_Cuisine.sort_values(by="Count",ascending=False)

cu_Koramangala

	Cuisine	Count
2	North Indian	20
6	Chinese	18
1	South Indian	13
13	Biryani	12
11	Desserts	12
3	Fast Food	11
17	American	7
12	Andhra	6
4	Beverages	5
8	Pizzas	5
14	Seafood	5
9	Italian	4
36	Ice Cream	3
20	Asian	3
19	Mughlai	3
18	Snacks	3
21	Continental	3
16	Healthy Food	3
10	Mexican	3
26	Salads	2
7	Thai	2
35	Indian	2
40	Pan-Asian	2
25	Cafe	2
33	Grill	1
34	Home Food	1
39	Kebabs	1
37	Juices	1
38	Chaat	1
31	Turkish	1
32	Portuguese	1
0	Sweets	1
30	Kerala	1
29	Hyderabadi	1
28	Punjabi	1
27	Pastas	1
24	Bakery	1
23	Lebanese	1
22	Mediterranean	1
15	Arabian	1
5	Jain	1
41	Oriental	1

fig = px.pie(data_frame = cu_HSR, 
             names = cu_Koramangala['Cuisine'], 
             values = cu_Koramangala['Count'], 
             title = 'Distribution of Cuisines in Area Koramangala Restaurants')

fig.update_traces(textposition = 'inside', textinfo = 'percent+label')
fig.show()

Conclusion:

We can say, In Koramangala Area, "North Indian" Cuisines are dominated by around '12%' followed by "Chinese" Cuisine is 10.8% & "South Indian" Cuisine is 7.78%

So, We can also infer that Most of the people are fond of the "North Indian" Cuisine

WordCloud Representation for Cuisines:¶

df

	Shop_Name	Cuisine	Location	Rating	Cost_for_Two	Area
0	Kanti Sweets	Sweets	Koramangala, Koramangala	4.3	150	Koramangala
1	Mumbai Tiffin	North Indian, Home Food, Thalis, Combo	Sector 5, HSR	4.4	400	HSR
2	Sri Krishna sagar	South Indian, North Indian, Fast Food, Beverag...	6th Block, Koramangala	4.1	126	Koramangala
3	Al Daaz	American, Arabian, Chinese, Desserts, Fast Foo...	HSR, HSR	4.4	400	HSR
4	Beijing Bites	Chinese, Thai	5th Block, Koramangala	4.1	450	Koramangala
...	...	...	...	...	...	...
113	Wok Paper Scissors	Pan-Asian, Chinese, Asian	JNC Road, Koramangala	3.9	219	Koramangala
114	Savoury Restaurant	Arabian, Middle Eastern, North Indian, Grill, ...	Madiwala, BTM	4.1	600	BTM
115	Royal Treat	North Indian, Chinese, Seafood, Biryani	5th block Koramangala, Koramangala	4.2	193	Koramangala
116	Thali 99	North Indian	Koramangala, Koramangala	4.3	200	Koramangala
117	Mani's Dum Biryani	Andhra, Biryani	1st Block, Koramangala	4.2	400	Koramangala

118 rows × 6 columns

df['Cuisine']

0                                                 Sweets
1                 North Indian, Home Food, Thalis, Combo
2      South Indian, North Indian, Fast Food, Beverag...
3      American, Arabian, Chinese, Desserts, Fast Foo...
4                                          Chinese, Thai
                             ...                        
113                            Pan-Asian, Chinese, Asian
114    Arabian, Middle Eastern, North Indian, Grill, ...
115              North Indian, Chinese, Seafood, Biryani
116                                         North Indian
117                                      Andhra, Biryani
Name: Cuisine, Length: 118, dtype: object

df_Swiggy_Text = ', '.join(df['Cuisine'])
df_Swiggy_Text

'Sweets, North Indian, Home Food, Thalis, Combo, South Indian, North Indian, Fast Food, Beverages, Jain, American, Arabian, Chinese, Desserts, Fast Food, Mughlai, North Indian, Chinese, Thai, North Indian, Fast Food, North Indian, Chinese, Italian, South Indian, Kerala, Chinese, North Indian, Pizzas, Italian, Mexican, Chinese, South Indian, North Indian, Desserts, Fast Food, Kerala, Andhra, Beverages, Mughlai, Seafood, Desserts, Chinese, Andhra, Biryani, Seafood, Chinese, South Indian, Chinese, Desserts, North Indian, Arabian, Fast Food, Desserts, Beverages, North Indian, North Indian, Indian, Rajasthani, Desserts, Bakery, Chinese, Healthy Food, North Indian, North Indian, Chinese, Hyderabadi, Fast Food, Chinese, Jain, North Indian, South Indian, Indian, North Indian, South Indian, Chinese, Andhra, Biryani, Chinese, Desserts, Fast Food, Seafood, South Indian, American, Fast Food, Biryani, Seafood, North Indian, Chinese, Desserts, Andhra, South Indian, Snacks, American, North Indian, South Indian, Kerala, South Indian, Mexican, North Indian, Chinese, Biryani, North Indian, Chinese, Biryani, North Indian, Chinese, Biryani, North Indian, Turkish, Portuguese, American, Biryani, Desserts, South Indian, Snacks, North Indian, Chinese, Desserts, Desserts, Fast Food, Sweets, Chaat, Chinese, South Indian, Andhra, Hyderabadi, Pizzas, Fast Food, Biryani, Mughlai, South Indian, Chinese, Asian, North Indian, Chinese, South Indian, Italian, Desserts, Pizzas, Biryani, Andhra, South Indian, Chinese, Continental, Italian, Mediterranean, Thai, Lebanese, American, Asian, Beverages, Bakery, Biryani, Cafe, Desserts, Healthy Food, Mexican, North Indian, Salads, Pizzas, Biryani, Pizzas, Chinese, Pastas, Salads, American, Continental, North Indian, American, Fast Food, South Indian, Andhra, Biryani, Chinese, South Indian, North Indian, Fast Food, Fast Food, Beverages, Desserts, Bakery, Biryani, South Indian, North Indian, Fast Food, Andhra, Beverages, Mughlai, Seafood, Punjabi, Hyderabadi, Chinese, Biryani, Chinese, Beverages, Chinese, North Indian, South Indian, Biryani, Kerala, North Indian, Chinese, Kerala, Chinese, North Indian, Chinese, Arabian, Beverages, Biryani, Chinese, Desserts, North Indian, Biryani, Juices, Kebabs, Andhra, South Indian, North Indian, American, Fast Food, Beverages, Cafe, Snacks, North Indian, South Indian, Turkish, Portuguese, American, Grill, Home Food, Healthy Food, Indian, Andhra, Biryani, Chinese, Desserts, Fast Food, Seafood, South Indian, Chinese, Thai, Chinese, Ice Cream, Chinese, Hyderabadi, Biryani, Indian, South Indian, Andhra, Tandoor, Punjabi, North Indian, Chinese, Fast Food, Healthy Food, Mughlai, Desserts, American, North Indian, Biryani, Hyderabadi, Andhra, North Indian, South Indian, Chinese, Fast Food, Juices, North Indian, North Indian, Chaat, Snacks, Fast Food, Desserts, Indian, Desserts, Mughlai, Seafood, Ice Cream, Desserts, Ice Cream, Desserts, Fast Food, Italian, Biryani, North Indian, North Indian, Snacks, American, Desserts, South Indian, Chinese, North Indian, Biryani, Kebabs, Andhra, Chettinad, Chinese, Mughlai, North Indian, Chettinad, South Indian, Continental, Indian, Pan-Asian, Oriental, North Indian, South Indian, North Indian, Biryani, North Indian, South Indian, Andhra, Biryani, Pan-Asian, Chinese, Asian, Arabian, Middle Eastern, North Indian, Grill, Seafood, Kerala, Chinese, North Indian, Chinese, Seafood, Biryani, North Indian, Andhra, Biryani'

from wordcloud import WordCloud

import nltk
from nltk.corpus import stopwords

import warnings
warnings.filterwarnings(action = 'ignore')

stopwords_ENG = set(stopwords.words('english'))
stopwords_ENG

{'a',
 'about',
 'above',
 'after',
 'again',
 'against',
 'ain',
 'all',
 'am',
 'an',
 'and',
 'any',
 'are',
 'aren',
 "aren't",
 'as',
 'at',
 'be',
 'because',
 'been',
 'before',
 'being',
 'below',
 'between',
 'both',
 'but',
 'by',
 'can',
 'couldn',
 "couldn't",
 'd',
 'did',
 'didn',
 "didn't",
 'do',
 'does',
 'doesn',
 "doesn't",
 'doing',
 'don',
 "don't",
 'down',
 'during',
 'each',
 'few',
 'for',
 'from',
 'further',
 'had',
 'hadn',
 "hadn't",
 'has',
 'hasn',
 "hasn't",
 'have',
 'haven',
 "haven't",
 'having',
 'he',
 'her',
 'here',
 'hers',
 'herself',
 'him',
 'himself',
 'his',
 'how',
 'i',
 'if',
 'in',
 'into',
 'is',
 'isn',
 "isn't",
 'it',
 "it's",
 'its',
 'itself',
 'just',
 'll',
 'm',
 'ma',
 'me',
 'mightn',
 "mightn't",
 'more',
 'most',
 'mustn',
 "mustn't",
 'my',
 'myself',
 'needn',
 "needn't",
 'no',
 'nor',
 'not',
 'now',
 'o',
 'of',
 'off',
 'on',
 'once',
 'only',
 'or',
 'other',
 'our',
 'ours',
 'ourselves',
 'out',
 'over',
 'own',
 're',
 's',
 'same',
 'shan',
 "shan't",
 'she',
 "she's",
 'should',
 "should've",
 'shouldn',
 "shouldn't",
 'so',
 'some',
 'such',
 't',
 'than',
 'that',
 "that'll",
 'the',
 'their',
 'theirs',
 'them',
 'themselves',
 'then',
 'there',
 'these',
 'they',
 'this',
 'those',
 'through',
 'to',
 'too',
 'under',
 'until',
 'up',
 've',
 'very',
 'was',
 'wasn',
 "wasn't",
 'we',
 'were',
 'weren',
 "weren't",
 'what',
 'when',
 'where',
 'which',
 'while',
 'who',
 'whom',
 'why',
 'will',
 'with',
 'won',
 "won't",
 'wouldn',
 "wouldn't",
 'y',
 'you',
 "you'd",
 "you'll",
 "you're",
 "you've",
 'your',
 'yours',
 'yourself',
 'yourselves'}

wordcloud = WordCloud(width = 1000, height = 500, stopwords = stopwords_ENG, background_color = 'white').generate(df_Swiggy_Text)
plt.figure(figsize = (22, 7))
plt.imshow(wordcloud)
plt.axis('off')
plt.show()

# Save the Image in the 'img' Folder:
wordcloud.to_file("D:/Prateek/Cuisines.png")

<wordcloud.wordcloud.WordCloud at 0x14507f790a0>